A comparison of sex, morphology, physiology and behavior of black-capped chickadees trapped using two common capture methods
Bibliographic record
Abstract
Many biological studies require the capture of individuals for sampling, for example for measurement of morphological or physiological traits, or for marking individuals for later observations. Capture methods employed often vary both within and between studies, and these differing methods could be more or less effective in capture of different individuals based on their morphology or behavior. If individuals that are prone to capture by the selected method differ with respect to traits of interest, such sampling bias could generate misleading or simply inaccurate results. The selection of capture methods could introduce two different forms of sampling bias, with the individuals that are sampled differing from the population at large or with individuals sampled via one method differing from individuals that could be sampled using a different method. We investigated this latter form of sampling bias by comparing individual birds sampled using two common capture techniques. We caught free-ranging black-capped chickadees ( Poecile atricapillus ) using walk-in traps baited with seed and mist nets paired with playback of an audio stimulus (conspecific mobbing calls). We measured 18 traits that we expect might vary among birds that are trappable by these differing methods—one that targets birds that are food motivated and potentially less neophobic and another that targets birds that respond readily to a perceived predation risk. We found no differences in the sex, morphology, initial and stress-induced corticosterone concentrations, behavioral response to a novel object, or behavioral response to a predator between individuals captured by these two methods. Individual variation in the behavioral response to a novel object was greater among birds caught by mist nets, suggesting this method might provide a sample that better reflects population-level individual variation. We do not know if the birds caught by these two methods provide a representative sample of the population at large, but can conclude that selection of either of these two common capture methods can similarly sample mean trait values of a population of interest. To accurately assess individual variation, particularly in behavior, mist nets might be preferable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".